Aeneas transforms how historians connect the past
We’re publishing a paper in Nature introducing Aeneas, the first AI model for contextualizing ancient inscriptions.
mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages
Knowledge Graphs represent real-world entities and the relationships between them. Multilingual Knowledge Graph Construction (mKGC) refers to the task of automatically constructing or predicting missing entities and links for knowledge graphs in a multilingual setting. In this work, we reformulate the mKGC task as a Question Answering (QA) task and introduce mRAKL: a Retrieval-Augmented Generation …
Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization
At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this …
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Gemini 2.5 Flash-Lite is now ready for scaled production use
Gemini 2.5 Flash-Lite, previously in preview, is now stable and generally available. This cost-efficient model provides high quality in a small size, and includes 2.5 family features like a 1 million-token context window and multimodality.
Beyond accelerators: Lessons from building foundation models on AWS with Japan’s GENIAC program
In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure …
25+ top gen AI how-to guides for enterprise
The best way to learn AI is by building. From finding quick ways to deploy open models to building complex, multi-agentic systems, it’s easy to feel overwhelmed by the sheer volume of resources out there. To that end, we’ve compiled a living, curated collection of our 25+ favorite how-to guides for Google Cloud. This collection …
Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad
Our advanced model officially achieved a gold-medal level performance on problems from the International Mathematical Olympiad (IMO), the world’s most prestigious competition for young mathematicians. It earned a total of 35 points by perfectly solving five out of the six problems.
On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization
The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of density-induced metrics on parameter spaces, to analyze existing methods within the space of model compression, primarily focusing on operator factorization. Adopting this perspective highlights the core challenge: …
Build an AI-powered automated summarization system with Amazon Bedrock and Amazon Transcribe using Terraform
Extracting meaningful insights from unstructured data presents significant challenges for many organizations. Meeting recordings, customer interactions, and interviews contain invaluable business intelligence that remains largely inaccessible due to the prohibitive time and resource costs of manual review. Organizations frequently struggle to efficiently capture and use key information from these interactions, resulting in not only productivity …
Language Models Improve When Pretraining Data Matches Target Tasks
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies, train models, measure benchmark performance, then refine accordingly. This raises a natural question: what happens when we make this optimization explicit? To explore this, we propose benchmark-targeted ranking (BETR), a simple method that …
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